INTELLIGENT MUSEUM MANAGEMENT SYSTEMS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7124Keywords:
Intelligent Museum Management Systems, Smart Museums, Artificial Intelligence, Internet of Things, Visitor Experience Personalization, Digital Heritage ManagementAbstract [English]
IMMS is an innovative paradigm in the management, conservation, and mediational experience of cultural heritage organizations. IMMS can help transform museums into adaptable, responsive, knowledge-oriented places through artificial intelligence, Internet of Things, data-driven analytics, and cyber-physical infrastructures. This paper conceptualizes IMMS to be socio-technical ecosystems that integrate digital representations of objects, real time monitoring of visitors and environments and intelligent decision engines used to support curatorial, conservation, and operational processes. The suggested framework presents a multi-layer structure including sensing and data management layers, intelligence, and application layers that allow a smooth interaction between physical assets of the museum and digital twins. Modern AI modules enable visitor-centered services based on behavioral profiling, recommendation of personal exhibitions, and adaptive content based on a person's emotions, which positively affect the level of engagement, inclusivity, and educational outcomes. At the same time, intelligent asset management operations enhance predictive preservation, environmental surveillance, and provenance authentication, which enhance heritage care. The operational intelligence capabilities would streamline the process of attendance forecasting, staffing, energy, and space utilization to achieve sustainability and cost-effectiveness. The study presents representative deployment situations to demonstrate quantifiable changes in the satisfaction of visitors, resource utilization, and decision quality along with practical issues connected to the integration of the data, ethical and institutional preparedness.
References
Bai, Y., Yang, X., Zhang, L., Zhang, R., Chen, N., and Dai, X. (2024). Carbon Emission Accounting and Decarbonization Strategies in Museum Industry. Energy Informatics, 7, 83. https://doi.org/10.1186/s42162-024-00403-6 DOI: https://doi.org/10.1186/s42162-024-00403-6
Cerquetti, M., Sardanelli, D., and Ferrara, C. (2024). Measuring Museum Sustainability Within the Framework of Institutional Theory: A Dictionary-Based Content Analysis of French and British National Museums' annual reports. Corporate Social Responsibility and Environmental Management, 31, 2260-2276. https://doi.org/10.1002/csr.2689 DOI: https://doi.org/10.1002/csr.2689
Choi, S., and Yoon, S. (2025). AI Agent-Based Intelligent Urban Digital Twin (I‑UDT): Concept, Methodology, and Case Studies. Smart Cities, 8, 28. https://doi.org/10.3390/smartcities8010028 DOI: https://doi.org/10.3390/smartcities8010028
Gaikwad, R. R., and Damodaran, D. (2024). The Rise of Predictive Analytics in Management Accounting: from Descriptive to Prescriptive. ShodhAI: Journal of Artificial Intelligence, 1(1), 159–167. https://doi.org/10.29121/shodhai.v1.i1.2024.54 DOI: https://doi.org/10.29121/shodhai.v1.i1.2024.54
Hou, Y. (2024). Application of Intelligent Internet of Things and Interaction Design in Museum Tour. Heliyon, 10, E35866. https://doi.org/10.1016/j.heliyon.2024.e35866 DOI: https://doi.org/10.1016/j.heliyon.2024.e35866
Jin, Y., Sharifi, A., Li, Z., Chen, S., Zeng, S., and Zhao, S. (2024). Carbon Emission Prediction Models: A Review. Science of the Total Environment, 927, 172319. https://doi.org/10.1016/j.scitotenv.2024.172319 DOI: https://doi.org/10.1016/j.scitotenv.2024.172319
Kudriashov, M. (2023). Eco-climatic Agenda of Italian Museums, Analysis, Best Practices, Perspectives and Recommendations (Master's thesis). Università degli Studi di Padova, Padua, Italy.
Liu, Z., and Chang, S. (2024). A Study of Digital Exhibition Visual Design led by Digital Twin and VR Technology. Measurement: Sensors, 31, 100970. https://doi.org/10.1016/j.measen.2023.100970 DOI: https://doi.org/10.1016/j.measen.2023.100970
Loupa, G., Dabanlis, G., Kostenidou, E., and Rapsomanikis, S. (2025). Air quality and energy use in a Museum. Air, 3, 5. https://doi.org/10.3390/air3010005 DOI: https://doi.org/10.3390/air3010005
Ullah, Z., Rehman, A. U., Wang, S., Hasanien, H. M., Luo, P., Elkadeem, M. R., and Abido, M. A. (2023). IoT-based Monitoring and Control of Substations and Smart Grids with Renewables and Electric Vehicles Integration. Energy, 282, 128924. https://doi.org/10.1016/j.energy.2023.128924 DOI: https://doi.org/10.1016/j.energy.2023.128924
Wang, S., Duan, Y., Yang, X., Cao, C., and Pan, S. (2023). 'Smart Museum' in China: From Technology Labs to Sustainable Knowledgescapes. Digital Scholarship in the Humanities, 38, 1340-1358. https://doi.org/10.1093/llc/fqac097 DOI: https://doi.org/10.1093/llc/fqac097
Wu, R., Gao, L., Lee, H., Xu, J., and Pan, Y. (2024). A Study of the Key Factors Influencing Young Users' Continued use of the Digital Twin-enhanced Metaverse Museum. Electronics, 13, 2303. https://doi.org/10.3390/electronics13122303 DOI: https://doi.org/10.3390/electronics13122303
Wu, Y., Jiang, Q., Liang, H. E., and Ni, S. (2022). What Drives Users to Adopt a Digital Museum? A Case of Virtual Exhibition Hall of National Costume Museum. Sage Open, 12, 21582440221082105. https://doi.org/10.1177/21582440221082105 DOI: https://doi.org/10.1177/21582440221082105
Wu, Y., Zhang, K., and Zhang, Y. (2021). Digital twin networks: A Survey. IeeE Internet of Things Journal, 8, 13789-13804. https://doi.org/10.1109/JIOT.2021.3079510 DOI: https://doi.org/10.1109/JIOT.2021.3079510
Zou, C., Rhee, S. Y., He, L., Chen, D., and Yang, X. (2024). Sounds of History: A Digital Twin Approach to Musical Heritage Preservation in virtual museums. Electronics, 13, 2388. https://doi.org/10.3390/electronics13122388 DOI: https://doi.org/10.3390/electronics13122388
Świt‑Jankowska, B. (2024). Adam Mickiewicz Museum in Śmiełów-towards a Contemporary Museum Concept Using Digital twin technology. Architectus, 79, 65-74. https://doi.org/10.37190/arc240307 DOI: https://doi.org/10.37190/arc240307
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Copyright (c) 2026 Dr. Khriereizhunuo Dzuvichu, Dr. R. Vasanthan, Vinitha M, Sulabha Narendra Patil, Naman Soni, Dr. Shweta Bajaj

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